{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3534e0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d6d0119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11490434/11490434 [==============================] - 11s 1us/step\n"
     ]
    }
   ],
   "source": [
    "#加载数据库\n",
    "mnist = keras.datasets.mnist\n",
    "(train_images,train_labels),(test_images, test_labels)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42fb0862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixels are normalized.\n"
     ]
    }
   ],
   "source": [
    "#归一化\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images/255.0\n",
    "print('Pixels are normalized.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82af1ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#打印图片\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.gray)\n",
    "    plt.xlabel(train_labels[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2f0cff6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1671 - accuracy: 0.9490\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0626 - accuracy: 0.9813\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0473 - accuracy: 0.9853\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0386 - accuracy: 0.9874\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0313 - accuracy: 0.9897\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " reshape (Reshape)           (None, 28, 28, 1)         0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 24, 24, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 12, 12, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 12, 12, 32)        0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 4608)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                46090     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 55,658\n",
      "Trainable params: 55,658\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#构建模型\n",
    "model=keras.Sequential([\n",
    "    keras.layers.InputLayer(input_shape=(28,28)),                                #输入层\n",
    "    keras.layers.Reshape(target_shape=(28,28,1)),                                #调整维度\n",
    "    keras.layers.Conv2D(filters=32,kernel_size=(3,3),activation=tf.nn.relu),     #\n",
    "    keras.layers.Conv2D(filters=32,kernel_size=(3,3),activation=tf.nn.relu),     #两个二维卷积层\n",
    "    keras.layers.MaxPooling2D(pool_size=(2,2)),                                  #池化\n",
    "    keras.layers.Dropout(0.25),                                                  #丢弃1/4神经元\n",
    "    keras.layers.Flatten(),                                                      #降维\n",
    "    keras.layers.Dense(10)                                                       #Dense作为输出层\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])\n",
    "model.fit(train_images, train_labels, epochs=5)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52e7666f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step - loss: 0.0343 - accuracy: 0.9888\n",
      "Test accuracy: 0.9887999892234802\n"
     ]
    }
   ],
   "source": [
    "#模型评估\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08fc7345",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "def get_label_color(val1, val2):\n",
    "    if(val1==val2):\n",
    "        return 'black'\n",
    "    else:\n",
    "        return 'red'\n",
    "\n",
    "predictions = model.predict(test_images)\n",
    "prediction_digits = np.argmax(predictions, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eaca4293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x1800 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#打印预测结果\n",
    "plt.figure(figsize=(18,18))\n",
    "for i in range(100):\n",
    "    ax=plt.subplot(10,10,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    image_index = random.randint(0,len(prediction_digits))\n",
    "    plt.imshow(test_images[image_index], cmap=plt.cm.gray)\n",
    "    ax.xaxis.label.set_color(get_label_color(prediction_digits[image_index],\\\n",
    "                                            test_labels[image_index]))\n",
    "    plt.xlabel('Predicted:%d' % prediction_digits[image_index])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc65d10d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\gzcai\\AppData\\Local\\Temp\\tmp8g_9d7o8\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\gzcai\\AppData\\Local\\Temp\\tmp8g_9d7o8\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Float model size = 221 KBs.\n"
     ]
    }
   ],
   "source": [
    "#将Kreas模型转换为TFLite格式\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_float_model = converter.convert()\n",
    "float_model_size=len(tflite_float_model)/1024\n",
    "print('Float model size = %d KBs.' %float_model_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "38e513fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\gzcai\\AppData\\Local\\Temp\\tmp_1i_cest\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\gzcai\\AppData\\Local\\Temp\\tmp_1i_cest\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantized model size = 59 KBs.\n",
      "which is about 26% of the float model size.\n"
     ]
    }
   ],
   "source": [
    "#使用量化技术缩小模型，并显示模型大小核新模型相对旧模型的大小比例\n",
    "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "tflite_quantized_model = converter.convert()\n",
    "quantized_model_size=len(tflite_quantized_model)/1024\n",
    "print('Quantized model size = %d KBs.' %quantized_model_size)\n",
    "print('which is about %d%% of the float model size.'\\\n",
    "     %(quantized_model_size *100 /float_model_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "382b74d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Float model Accuracy = 0.9888\n",
      "Quantized model Accuracy = 0.9887\n",
      "Accuracy drop = 0.0001\n"
     ]
    }
   ],
   "source": [
    "#使用test测试集来评估模型\n",
    "def evaluate_tflite_model(tflite_model):\n",
    "    interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
    "    interpreter.allocate_tensors()\n",
    "    input_tensor_index = interpreter.get_input_details()[0][\"index\"]\n",
    "    output = interpreter.tensor(interpreter.get_output_details()[0][\"index\"])\n",
    "    \n",
    "    prediction_digits = []\n",
    "\n",
    "    #对测试数据集中的每张图都进行预测\n",
    "    for test_image in test_images:\n",
    "        test_image = np.expand_dims(test_image, axis=0).astype(np.float32)\n",
    "        interpreter.set_tensor(input_tensor_index, test_image)\n",
    "\n",
    "        #使用模型进行推断\n",
    "        interpreter.invoke()\n",
    "\n",
    "        #找到概率最高的数字作为预测结果\n",
    "        digit = np.argmax(output()[0])\n",
    "        prediction_digits.append(digit)\n",
    "\n",
    "    #初始化正确率\n",
    "    accurate_count  = 0\n",
    "\n",
    "    #计算预测精度\n",
    "    for index in range(len(prediction_digits)):\n",
    "        if prediction_digits[index] == test_labels[index]:\n",
    "            accurate_count += 1\n",
    "    accuracy = accurate_count * 1.0 / len(prediction_digits)\n",
    "    \n",
    "    return accuracy\n",
    " \n",
    "#评估TFLite浮点模型\n",
    "float_accuracy = evaluate_tflite_model(tflite_float_model)\n",
    "print('Float model Accuracy = %.4f' % float_accuracy)\n",
    "\n",
    "#评估TFLite量化模型\n",
    "quantized_accuracy = evaluate_tflite_model(tflite_quantized_model)\n",
    "print('Quantized model Accuracy = %.4f' % quantized_accuracy)\n",
    "print('Accuracy drop = %.4f'%(float_accuracy-quantized_accuracy))\n",
    "#使用模型预测测试集每张图片的标签，并与真实标签进行比对，以此来评估模型精度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5b088d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "#将量化模型保存至文件中\n",
    "f=open('mnist.tflite',\"wb\")\n",
    "f.write(tflite_quantized_model)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02d83ef6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
